Cooperative Position and Orientation Estimation with Multi-Mode Antennas

Robotic multi-agent systems are envisioned for planetary exploration and terrestrial applications. Autonomous operation of robots requires estimations of their positions and orientations, which are obtained from the direction-of-arrival (DoA) and the time-of-arrival (ToA) of radio signals exchanged among the agents. In this thesis, we estimate the signal DoA and ToA using a multi-mode antenna (MMA). An MMA is a single antenna element, where multiple orthogonal current modes are excited by different antenna ports. We provide a first study on the use of MMAs for cooperative position and orientation estimation, specifically exploring their DoA estimation capabilities. Assuming the agents of a cooperative network are equipped with MMAs, lower bounds on the achievable position and orientation accuracy are derived. We realize a gap between the theoretical lower bounds and real-world performance of a cooperative radio localization system, which is caused by imperfect antenna and transceiver calibration. Consequentially, we theoretically analyze in-situ antenna calibration, introduce an algorithm for the calibration of arbitrary multiport antennas and show its effectiveness by simulation. To also improve calibration during operation, we propose cooperative simultaneous localization and calibration (SLAC). We show that cooperative SLAC is able to estimate antenna responses and ranging biases of the agents together with their positions and orientations, leading to considerably better position and orientation accuracy. Finally, we validate the results from theory and simulation by experiments with robotic rovers equipped with software-defined radios (SDRs). In conclusion, we show that DoA estimation with an MMA is feasible, and accuracy can be improved by in-situ calibration and SLAC

Power-Based Direction-of-Arrival Estimation Using a Single Multi-Mode Antenna

Phased antenna arrays are widely used for direction-of-arrival (DoA) estimation. For low-cost applications, signal power or received signal strength indicator (RSSI) based approaches can be an alternative. However, they usually require multiple antennas, a single antenna that can be rotated, or switchable antenna beams. In this paper we show how a multi-mode antenna (MMA) can be used for power-based DoA estimation. Only a single MMA is needed and neither rotation nor switching of antenna beams is required. We derive an estimation scheme as well as theoretical bounds and validate them through simulations. It is found that power-based DoA estimation with an MMA is feasible and accurate

Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation

Multi-mode antennas are an alternative to classical antenna arrays, and hence a promising emerging sensor technology for a vast variety of applications in the areas of array signal processing and digital communications. An unsolved problem is to describe the radiation pattern of multi-mode antennas in closed analytic form based on calibration measurements or on electromagnetic field (EMF) simulation data. As a solution, we investigate two modeling methods: One is based on the array interpolation technique (AIT), the other one on wavefield modeling (WM). Both methods are able to accurately interpolate quantized EMF data of a given multi-mode antenna, in our case a planar four-port antenna developed for the 6-8.5 GHz range. Since the modeling methods inherently depend on parameter sets, we investigate the influence of the parameter choice on the accuracy of both models. Furthermore, we evaluate the impact of modeling errors for coherent maximum-likelihood direction-of-arrival (DoA) estimation given different model parameters. Numerical results are presented for a single polarization component. Simulations reveal that the estimation bias introduced by model errors is subject to the chosen model parameters. Finally, we provide optimized sets of AIT and WM parameters for the multi-mode antenna under investigation. With these parameter sets, EMF data samples can be reproduced in interpolated form with high angular resolution

The Role of Time in a Robotic Swarm: A Joint View on Communications, Localization, and Sensing

Autonomous robotic swarms are envisioned for a variety of sensing applications in space exploration, search-and-rescue and disaster management. An important capability of a swarm is sensing spatio-temporal processes such as radio wave propagation or seismic activities. The spatio-temporal properties of these processes dictate the required sensing position and time accuracy, as well as update rate. A dedicated wireless communication system needs to be jointly designed for swarm information exchange, self-localization and sensing. In this article, we emphasize the role of time in a robotic swarm. We introduce the system ingredients and dive into realistic clock models. Clock models and channel access scheme decisively influence the swarm self-localization and synchronization accuracy, and consequently the swarm sensing performance. Finally, we discuss practical implementation aspects, introduce our developed swarm radio system, and give an outlook on a moon-analogue exploration mission

Cooperative Communication, Localization, Sensing and Control for Autonomous Robotic Networks

Networks composed of a myriad of autonomous robots have attracted increasing attention in recent years due to their enormous capability expansion from single robot systems. In these networks, robots benefit from the collaboration with each other to enhance their situation awareness for autonomous operation. For example, in an extraterrestrial exploration mission, a robotic swarm can collaboratively utilize the inter-robot communication system to propagate information, synchronize and navigate itself to achieve mission objectives like joint environmental sensing. In addition, each robot can decide and control its own trajectory, so that the aforementioned tasks are accomplished in a globally efficient manner. In this paper, we propose formation optimization strategies for autonomous robotic networks, which adapt to the mission demands on cooperative communication, localization and sensing. We also discuss three space exploration examples with different mission demands, which leads to distinct network formations. These three missions will be conceptually demonstrated in a space analog mission on the volcano Mount Etna in June 2022

Simultaneous Localization and Antenna Calibration

Cooperative localization fills the gap in scenarios where global navigation satellite system (GNSS) reception is denied or impaired. Position and orientation information is then often provided based on signal round-trip time (RTT) and direction-of-arrival (DoA). Obtaining a meaningful RTT requires calibrated transceiver group delays, and accurate DoA estimation requires antenna calibration. Usually, such calibrations are performed once before operation. However, calibration parameters can change over time, e.g. due to varying temperature of RF components or reconfigurable antenna surroundings. To cope with that, we propose to estimate antenna responses and ranging biases simultaneously with positions and orientations by simultaneous localization and calibration (SLAC). We derive a SLAC algorithm based on Bayesian filtering, which is suitable for arbitrary antenna types. The algorithm is evaluated with measurement data from robotic rovers. We show, that ranging and DoA performance is improved considerably, leading to better position and orientation accuracy with SLAC

Simultaneous Localization and Calibration for Cooperative Radio Navigation

Cooperative radio localization and navigation systems can be used in scenarios where the reception of global navigation satellite system (GNSS) signals is not possible or impaired. While the benefit of cooperation has been highlighted by many papers, calibration is not widely considered, but equally important in practice. Utilizing the signal propagation time requires group delay or ranging bias calibration and estimating the direction-of-arrival (DoA) requires antenna response calibration. Often, calibration parameters are determined only once before operation. However, the calibration parameters are influenced by e.g. changing temperatures of radio frequency (RF) components or changing surroundings of antennas. To cope with that, we derive a cooperative simultaneous localization and calibration (SLAC) algorithm based on Bayesian filtering, which estimates antenna responses and ranging biases simultaneously with positions and orientations. By simulations, we show that the calibration parameters can be estimated during operation without additional sensors. We further proof practical applicability of SLAC by evaluating measurement data from robotic rovers. With SLAC, both ranging and DoA estimation performance is improved, resulting in better position and orientation estimation accuracy. SLAC is thus able to provide reliable calibration and to mitigate model mismatch. Finally, we discuss open research questions and possible extensions of SLAC

Autonomous Navigation of a Robotic Swarm in Space Exploration Missions

In recent years the paradigm of navigation has shifted from pinpointing the location of a single agent to continuously estimating the full kinematic state of a team of autonomous agents. In this paper, we propose a kinematic-aware information seeking algorithm for a robotic swarm. The algorithm tightly couples state estimation and autonomous control given ranging and kinematic models. With the help of the Fisher information theory, agents generate information seeking command sequence on their actuators, which leads to smooth trajectories. As an outcome, the swarm continuously optimizes its formation so that the agents’ position and orientation uncertainty is actively minimized. The proposed algorithm is verified by physics simulations and demonstrated in a space-analog mission of autonomous swarm navigation on volcano Mount Etna

Cooperative Pose Estimation in a Robotic Swarm: Framework, Simulation and Experimental Results

Swarm robotics has gained an increasing attention in applications like extraterrestrial exploration and disaster management, due to the ability of simultaneously observing at different locations and avoiding a single point of failure. In order to operate autonomously, robots in a swarm need to know their precise poses, including their positions, velocities and orientations. When external navigation infrastructures like the global navigation satellite systems (GNSS) are not ubiquitously accessible, the swarm of robots need to rely on internal measurements to estimate their poses. In this paper, we propose a cooperative 3D pose estimation framework, based on the insights of sensor characteristics that we gained from outdoor swarm navigation experiments. A decentralized particle filter (DPF) operates on each robot to estimate its pose via fusing radio-based ranging, inertial sensor data, control commands and the pose estimates of its neighbors. This framework is integrated in the swarm navigation ecosystem developed at the German Aerospace Center (DLR), and is unified for both simulations and experiments

Cooperative Radio Navigation for Robotic Exploration: Evaluation of a Space-Analogue Mission

Autonomous robotic systems will play an important role in future planetary exploration missions. To allow autonomous operation of robots, reliable navigation is vital. Such a navigation solution is provided by cooperative radio navigation, where radio signals are exchanged among the robots. Based on the signal round-trip time (RTT) and direction-of-arrival (DoA), the robots' positions and orientations are estimated. Cooperative navigation has been well studied theoretically, but experiments mainly focused on indoor scenarios and other applications. For the first time, we have demonstrated cooperative radio navigation within a space-analogue exploration mission with two robotic rovers. The mission took place on the volcano Mt Etna, Sicily, Italy. During the first part of the mission, simultaneous localization and calibration (SLAC) is performed to improve the accuracy of RTT and DoA estimates by reducing the bias. Then, the rovers travel to a distant area of interest. Ultimately, one rover travels so far that it is connected to the network only via another rover. We find that even in this challenging single-link scenario, robust cooperative navigation is achieved. When the rovers are not further than 60 m away from the lander, their position root-mean-square errors (RMSEs) are 0.3m to 0.9m. Even for the most challenging mission phase, when the rovers are 100 m to 160 m away from the lander with single-link localization, the position RMSEs are 1.7m to 2.6m. The orientation RMSEs of the rovers lie between 2.4° to 6.1°. Thus, with this space-analogue mission, we show that cooperative radio navigation for planetary exploration is robust and accurate